Examining Sensory-motor Training Versus Impairment-based Training on Pain and Function in Subjects With Knee Osteoarthritis
Bibliographic record
Abstract
Purpose: Osteoarthritis is a degenerative joint disease affecting synovial joints and damages joints due to stresses caused by an abnormality in any of the synovial joint tissues, including articular cartilage and subchondral bone, ligaments, menisci, periarticular muscles, peripheral nerves, and synovium. To compare the effect of sensory-motor training versus impairment-based training on pain and physical function in subjects with knee osteoarthritis. Methods: Simple random sampling was used to divide 30 subjects aged 50 and 70 years who met the inclusion and exclusion criteria into two groups (n=15). Group A received sensory-motor training, while Group B received impairment-based training. Before the treatment, the subjects walked for 10 minutes to warm up. For three weeks, each group was treated three times per week. Pre-test; post-test outcomes are noted, the visual analog scale (VAS) was used to assess pain, and the Western Ontario and McMaster Universities osteoarthritis index (WOMAC) was used to assess physical function. Results: Comparing the post-test pain (VAS) scores between groups showed that the mean±SD of posttest pain (VAS) score in the sensory-motor training group was 2.707±1.01. The mean±SD in the impairment-based training group was almost the same, at 2.29±1.13. It was statistically significant at the 5% level. Similarly, the mean±SD of the posttest function (WOMAC) score in the sensory-motor training group was 16.55±6.92. Conclusion: Sensory-motor training is superior to impairment-based training on pain and physical function in subjects with knee osteoarthritis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".